code: def __call__(self, imgs):
      n_seeds = len(self.seeds_)
      imgs = check_niimg_4d(imgs, dtype=self.dtype)
  
      signals = np.empty(
          (imgs.shape[3], n_seeds), dtype=img_data_dtype(imgs)
      )
      for i, sphere in enumerate(
          _iter_signals_from_spheres(
              self.seeds_,
              imgs,
              self.radius,
              self.allow_overlap,
              mask_img=self.mask_img,
          )
      ):
          signals[:, i] = np.mean(sphere, axis=1)
      return signals, None
  
imports: import warnings
  import numpy
  from joblib import Memory
  from scipy import sparse
  from sklearn import neighbors
  from nilearn import image, masking
  from nilearn._utils import CacheMixin, fill_doc, logger
  from nilearn._utils.class_inspect import get_params
  from nilearn._utils.niimg import img_data_dtype
  from nilearn._utils.niimg_conversions import check_niimg_3d, check_niimg_4d, safe_get_data
  from nilearn.datasets import load_mni152_template
  from nilearn.maskers._utils import compute_middle_image
  from nilearn.maskers.base_masker import BaseMasker, _filter_and_extract
label_desc: Use image node_id 2 for calling the _ExtractionFunctor obj's underlying member method code with example usage: obj.__call__(imgs) and returns: signals,  None